Differences Between Data Analytics vs Data Analysis
Data analysis is a procedure of investigating, cleaning, transforming, and training of the data with the aim of finding some useful information, recommend conclusions and helps in decision-making. Data analysis tools are Open Refine, Tableau public, KNIME, Google Fusion Tables, Node XL and many more. Analytics is utilizing data, machine learning, statistical analysis and computer-based models to get better insight and make better decisions from the data. Analytics is defined as “a process of transforming data into actions through analysis and insight in the context of organisational decision making and problem-solving.” Analytics is supported by many tools such as Microsoft Excel, SAS, R, Python(libraries), tableau public, Apache Spark, and excel.
Head to Head Comparison Between Data Analytics and Data Analysis
Below are the top 6 differences between Data Analytics and Data Analysis:
Key Differences Between Data Analytics and Data Analysis
Below are the lists of points, describe the key Differences Between Data Analytics and Data Analysis:
- Data analytics is a conventional form of analytics which is used in many ways like health sector, business, telecom, insurance to make decisions from data and perform necessary action on data. Data analysis is a specialized form of data analytics used in businesses and other domain to analyze data and take useful insights from data.
- Data analytics consist of data collection and in general inspect the data and it has one or more usage whereas Data analysis consists of defining a data, investigation, cleaning the data by removing Na values or any outlier present in a data, transforming the data to produce a meaningful outcome.
- To perform data analytics, one has to learn many tools to perform necessary action on data. To achieve analytics, one must have knowledge of R, Python, SAS, Tableau Public, Apache Spark, Excel and many more. For data analysis, one must have hands-on of tools like Open Refine, KNIME, Rapid Miner, Google Fusion Tables, Tableau Public, Node XL, Wolfram Alpha tools etc.
- Data analytics life cycle consists of Business Case Evaluation, Data Identification, Data Acquisition & Filtering, Data Extraction, Data Validation & Cleansing, Data Aggregation & Representation, Data Analysis, Data Visualization, Utilization of Analysis Results. As we know that data analysis is a sub-component of data analytics so data analysis life cycle also comes into analytics part, it consists data gathering, data scrubbing, analysis of data and interprets the data precisely so that you can understand what your data want to say.
- Whenever someone wants to find that what will happen next or what is going to be next then we go with data analytics because data analytics helps to predict the future value. Whereas In data analysis, analysis performs on past dataset to understand what happened so far from data. Data analytics and data analysis both are necessary to understand the data one can be useful for estimating future demands and other is important for performing some analysis on data to look into past.
Data Analytics and Data Analysis Comparison Table
Below is the comparison table Between Data Analytics and Data Analysis.
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Basis for Comparison
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Data Analytics
|
Data Analysis
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| Form
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Data analytics is ‘general’ form of analytics which is used in businesses to make decisions from data which are data-driven | Data analysis is a specialized form of data analytics used in businesses to analyze data and take some insights of it. |
|
Structure |
Data analytics consist of data collection and inspect in general and it has one or more users. | Data analysis consisted of defining a data, investigation, cleaning, transforming the data to give a meaningful outcome.
|
| Tools | There are many analytics tools in a market but mainly R, Tableau Public, Python, SAS, Apache Spark, Excel are used. | For analyzing555555555555566 the data OpenRefine, KNIME, RapidMiner, Google Fusion Tables, Tableau Public, NodeXL, WolframAlpha tools are used. |
| Sequence | Data analytics life cycle consist of Business Case Evaluation, Data Identification, Data Acquisition & Filtering, Data Extraction, Data Validation & Cleansing, Data Aggregation & Representation, Data Analysis, Data Visualization, Utilization of Analysis Results.
|
The sequence followed in data analysis are data gathering, data scrubbing, analysis of data and interpret the data precisely so that you can understand what your data want to say. |
| Usage | Data Analytics, in general, can be used to find masked patterns, anonymous correlations, customer preferences, market trends and other necessary information that can help to make more notify decisions for business purpose. | Data analysis can be used in various ways like one can perform analysis like descriptive analysis, exploratory analysis, inferential analysis, predictive analysis and take useful insights from the data. |
| Example | Let say you have 1gb customer purchase related data of past 1 year, now one has to find that what our customers next possible purchases, you will use data analytics for that. | Suppose you have 1gb customer purchase related data of past 1 year and you are trying to find what happened so far that means in data analysis we look into past. |
Conclusion
Today data usage is rapidly increasing and a huge amount of data is collected across organizations. data can be related to customers, business purpose, applications users, visitors related and stakeholders etc. This data is churned and divided to find, understand and analyze patterns. Data analytics refers to various tools and skills involving qualitative and quantitative methods, which employ this collected data and produce an outcome which is used to improve efficiency, productivity, reduce risk and rise business gain. Data analytics techniques differ from organization to organization according to their demands.
Data analysis is a sub-component of data analytics is specialized decision-making tool which uses different technologies like tableau public, Open Refine, KNIME, Rapid Miner etc. and are useful in when performing exploratory analysis and produce some insights from data using a cleaning, transforming, modeling and visualizing the data and produce outcomes.
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